共享环境中人类与四足机器人的行为冲突规避
Behavioral Conflict Avoidance Between Humans and Quadruped Robots in Shared Environments
AI总结：
针对共享环境中四足机器人与人类行为冲突的安全问题，提出基于趋势适应的主动冲突规避框架，结合改进的低成本LiDAR人体检测与势场图选点方法，可主动规避冲突并快速恢复任务，有效降低干扰、提升人机安全性。
摘要：
如今，机器人越来越多地在与人类共享的环境中运行，人机行为冲突可能会危及安全。本文提出了一种基于趋势适应原则的四足机器人主动式行为冲突规避框架，该框架既能保障机器人安全，又能将对人类活动的干扰降至最低。无论机器人处于静止还是运动状态，它都能主动规避与靠近的人类或其他动态物体的潜在冲突，待冲突消退后迅速恢复任务。\n本文提出了一种改进方法，可在配备低成本混合固态LiDAR的振动式机器人平台上实现精准的人体检测与跟踪。当检测到潜在冲突时，机器人会选择规避点并执行规避机动，随后恢复任务。该方法有别于传统的目标驱动型方法，后者常出现激进行为，例如强行绕过障碍物进而引发冲突，或是陷入死锁场景。\n规避点的选择通过整合静态与动态障碍物生成势场图来实现，机器人随后在该图中搜索可行区域，并利用评价函数确定最优规避点。实验结果表明，该框架显著减少了对人类活动的干扰，提升了机器人与人员双方的安全性。
英文摘要：
Nowadays, robots are increasingly operated in environments shared with humans, where conflicts between human and robot behaviors may compromise safety. This paper presents a proactive behavioral conflict avoidance framework based on the principle of adaptation to trends for quadruped robots that not only ensures the robot's safety but also minimizes interference with human activities. It can proactively avoid potential conflicts with approaching humans or other dynamic objects, whether the robot is stationary or in motion, then swiftly resume its tasks once the conflict subsides. An enhanced approach is proposed to achieve precise human detection and tracking on vibratory robot platform equipped with low-cost hybrid solid-state LiDAR. When potential conflict detected, the robot selects an avoidance point and executes an evasion maneuver before resuming its task. This approach contrasts with conventional methods that remain goal-driven, often resulting in aggressive behaviors, such as forcibly bypassing obstacles and causing conflicts or becoming stuck in deadlock scenarios. The selection of avoidance points is achieved by integrating static and dynamic obstacle to generate a potential field map. The robot then searches for feasible regions within this map and determines the optimal avoidance point using an evaluation function. Experimental results demonstrate that the framework significantly reduces interference with human activities, enhances the safety of both robots and persons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
